code: def test_post_processing_train_df_list(self):
      dates = pd.date_range("1/1/2019", periods=8)
      values = np.random.randn(8)
      dt_col = "datetime"
      value_col = "values"
      df = pd.DataFrame({dt_col: dates, value_col: values})
  
      past_seq_len = 2
      future_seq_len = 1
      config = {
          "selected_features": json.dumps(
              [
                  "IS_AWAKE(datetime)",
                  "IS_BUSY_HOURS(datetime)",
                  "HOUR(datetime)",
              ]
          ),
          "past_seq_len": past_seq_len,
      }
      feat = TimeSequenceFeatureTransformer(
          future_seq_len=future_seq_len,
          dt_col="datetime",
          target_col="values",
          drop_missing=True,
      )
      df_list = [df] * 3
      train_x, train_y = feat.fit_transform(df_list, **config)
      y_unscale, y_unscale_1 = feat.post_processing(
          df_list, train_y, is_train=True
      )
      y_input = df[past_seq_len:][[value_col]].values
      target_y = np.concatenate([y_input] * 3)
      msg = "y_unscale is {}, y_unscale_1 is {}".format(
          y_unscale, y_unscale_1
      )
      assert_array_almost_equal(y_unscale, y_unscale_1, decimal=2), msg
      msg = "y_unscale is {}, y_input is {}".format(y_unscale, target_y)
      assert_array_almost_equal(y_unscale, target_y, decimal=2), msg
  
imports: import pytest
  from test.zoo.pipeline.utils.test_utils import ZooTestCase
  from zoo.chronos.autots.deprecated.feature.utils import save, restore
  from zoo.chronos.autots.deprecated.feature.time_sequence import *
  from numpy.testing import assert_array_almost_equal
  import json
  import tempfile
  import shutil
  from zoo.chronos.autots.deprecated.preprocessing.utils import train_val_test_split
label_desc: Use image node_id 15 for calling the TestTimeSequenceFeature obj's underlying member method code with example usage: obj.test_post_processing_train_df_list() without return types